Task : Try to predict major earthquake event¶

The task:
You are given real measurements collected on a course of 30 years and transformed into a classification problem by the following: A major event is defined as any reading of over 5 on the Richter Scale. To ignore aftershocks, a positive case is considered to be one where a major event is not preceded by another major event for at least 512 hours. Negative cases are instances with readings below 4 (to avoid blurring of the boundaries between major and non major events) that are preceded by at least 20 readings in the previous 512 hours that are non-zero (to avoid trivial negative cases). None of the cases overlap in time.

You are given a training file and a testing file for the above data. In the files, each set of 512 measurements is followed by a label: 1 for major event, 0 otherwise.

You should develop a solution in python for the above prediction problem. You should train your solution (only) on the training data and report accuracy measurements when running you solution on the testing data. Your submission should include your running code and a pdf explaining your solution, the results and your recommended next steps.

Source¶

I found this dataset here - http://timeseriesclassification.com/description.php?Dataset=Earthquakes
Notice that the best accuracy is 75.92%. If exists, it’s good to know the current state of the art scores on your dataset to be able to compare your results.

Richter Scale¶

I read a bit about Richter Scale.
I understand that it's a logarithmic scale. In many ML algorithms, distance measure between a pair of samples is done by Euclidean distance. But in our case of logarithmic scale, I'm not sure how it will effect the loss functions and inner calculation of the ML models (For example, in logarithmic scale, 5-3 >> 3-1).
We also need to deal with negative numbers. In Richter Scale, negative numbers are used to represent scale that is smaller than 1. (for example 1/10 as -1 etc.)
Maybe we need to upscale all the measurements.

Summary of the notebook:¶

Show me the data! - Present numeric statistics on the train and test datasets.
Testset: "Trainset, do you really know me well enough?" - Try to describe the numeric statistics, and see the differences between the train and the test sets. This is done to understand if we can infer something useful on the test set by learning on the training set. In this phase we will plot the train and test statistics (like mean, std, quartiles etc) in the same plot and try to identify similarity.
Show me the classes! - Now we will look only at the train set, and try to understand how the two classes (Earthquake, No-Earthquake) looks like. We will present the ratio between the classes. In addition, will will take 5 random samples from each class(all samples from the train set) and plot each couple of them (one from Earthquake class, and one from No-Earthquake class) and look for some visible patterns.
Features vs Features - We will want to know if the features are correlated, so for each class we'll calculate the correlation matrix between all of the 512 features, and print the highest values and plotting the correlation matrix. By doing it, we can find more patterns in the data, maybe to collapse few consecutive measurements into one value or remove some correlated features.
Features, describe yourselfs! - To better understand the features, we will print a boxplot for each feature. This way we can see if the data is corrupted with noisy measurements, understand the scale of each feature and identify outliers.
Can we finally start playing with ML??? - Now when we have better understanding of the data, we will apply some out of the box machine learning algorithms.
It's all about perspective - After investigating the ML results, we will try to improve them by applying transformations (Standardize features, Furie transformation) on the data and apply the previous ML algorithms again and investigate the results.
Now some deep learning (LSTM) for the fun.
Run all combinations - Auto-Sklearn is a module that try to search for an ensemble of classifiers from sklearn library. I run it for 24 hours.
If I had infinite amount of time - Future work

1.Show me the data!¶

Present numeric statistics on the train and test datasets.

Read the data and pring the train set

import pandas as pd
import os


train_file_name = "Student Hiring Project 2017 - Training Data.txt"
train_file_path = os.path.join("..","data",train_file_name)
train_data = pd.read_csv(train_file_path, sep=",", header=None)

test_file_name = "Student Hiring Project 2017 - Testing Data.txt"
test_file_path = os.path.join("..","data",test_file_name)
test_data = pd.read_csv(test_file_path, sep=",", header=None)
train_data

Print some statistics on the train set

train_data.describe()

Print some statistics on the test set

test_data.describe()

Testset: "Trainset, do you really know me well enough?"¶

Try to describe the numeric statistics, and see the differences between the train and the test sets. This is done to understand if we can infer something useful on the test set by learning on the training set. In this phase we will plot the train and test statistics (like mean, std, quartiles etc) in the same plot and try to identify similarity.

Results:¶

From the findings below, it seems that the train and test sets aren't that similar.

Print a plot for each statistic in the data description¶

X - feature number, Y - measurement value, Color - Blue=Trainset,Orange-Testset

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import figure


font = {'family': 'serif',
            'color':  'darkred',
            'weight': 'normal',
            'size': 30,
            }


def print_statistic_plot(index):
    """
        Insert a number from 1 to 7 it will print the appropriate plot
        1. mean
        2. std
        3. min
        4. 25%
        5. 50%
        6. 75%
        7. max
    """



    details_train = train_data.describe()
    details_test = test_data.describe()
    details_names = ["Mean","std","Min","25%","50%","75%","Max"]
    rows_train = details_train.iloc[index,:].values.tolist()[:-1]
    rows_test = details_test.iloc[index,:].values.tolist()[:-1]
    figure(figsize=(30,20))
    plt.title(details_names[index-1], fontdict=font)
    plt.xlabel('Feature', fontdict=font)
    plt.ylabel('Measurement', fontdict=font)
    
    plt.plot(rows_train, label="Train %s"%details_names[index-1])
    plt.plot(rows_test, label="Test %s"%details_names[index-1])
    plt.tick_params(axis='both', which='major', labelsize=20) # increase axis size

    leg = plt.legend(loc='upper right', ncol=2, shadow=True, fancybox=True,prop={'size': 30})
    plt.show()

print_statistic_plot(1) # mean

We can see that most of the mean values are bit more extreme in the test set(Yellow). Maybe due to the sample size. (smaller set size, more extreme conclusions).

print_statistic_plot(2) # std

Like the mean, we can see that most of the std values are bit more extreme in the test set(Yellow). Maybe due to the sample size. (smaller set size, more extreme conclusions).

print_statistic_plot(3) # min

min is lower in train (make sense, more examples in the train set, so more probability for extreme cases).

print_statistic_plot(7) # max

Like in min, max is higher in train (make sense, more examples in the train set, so more probability for extreme cases).
we also know that the calculation of std for a sample contains division by n-1 and not by n(like for population or for mean of a sample), this is due to the same reason.

print_statistic_plot(4) # 25%

25% quartile - Higher in train. (make sense with previous statement about means and std)

print_statistic_plot(5) # 50%

50% quartile -higher in train. I don't think it's a good thing. May indicate that the train doesn't represent the test well enough.

print_statistic_plot(6) # 75%

75% quartile - Most of the features are the same in train and test

Final conclusions:¶

Maybe we will need to group, shuffle and re-split the train and test sets to get more similar distributions.

3. Show me the classes!¶

Now we will look only at the train set, and try to understand how the two classes (Earthquake, No-Earthquake) looks like. We will present the ratio between the classes. In addition, will will take 5 random samples from each class(all samples from the train set) and plot each couple of them (one from Earthquake class, and one from No-Earthquake class) and look for some visible patterns.

Print classes ratio¶

one_label_count_train = train_data.loc[train_data[512] == 1].shape[0]
print("Train")
print("Number of rows with 0 label: ", train_data.shape[0] - one_label_count_train)
print("Number of rows with 1 label: ", one_label_count_train)
print("Total number of rows:        ",train_data.shape[0])


one_label_count_test = test_data.loc[test_data[512] == 1].shape[0]
print("\n\nTest")
print("Number of rows with 0 label: ", test_data.shape[0] - one_label_count_test)
print("Number of rows with 1 label: ", one_label_count_test)
print("Total number of rows:        ",test_data.shape[0])

Train
Number of rows with 0 label:  264
Number of rows with 1 label:  58
Total number of rows:         322


Test
Number of rows with 0 label:  104
Number of rows with 1 label:  35
Total number of rows:         139

Of course there are more no-earthquakes records (0 label) that earthquakes ones(1 label), but the difference isn't too dramatic (1:6 ratio in train, 1:4 in test). It's a bit weird, I would expect the ratio to be more extreme (like 1:100 or more).

Plot few positive and negative examples from the train set¶

We will print 5 plots. Each plot contains 2 samples, one Earthquake(label 1, purple color) and one No-Earthquake(label 0, green color). We will try to see if there is some noticeable differences just by looking at the data. X - Features, Y - Measurement value.

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import figure
from random import randint

positive_train = train_data.loc[train_data[512]==1]
positive_train
negative_train = train_data.loc[train_data[512]==0]
negative_train


for i in range(5):
    index = randint(0, min(positive_train.shape[0],negative_train.shape[0])-1)
    row_positive_train = positive_train.iloc[index,:].values.tolist()[:-1]
    row_negative_train = negative_train.iloc[index,:].values.tolist()[:-1]
    figure(figsize=(30,20))
    plt.xlabel('Feature', fontdict=font)
    plt.ylabel('Measurement', fontdict=font)
    plt.plot(row_positive_train,  color='purple', label="Earthquake")
    plt.plot(row_negative_train,  color='Green', label="No-Earthquake")
    leg = plt.legend(loc='upper right', ncol=2, shadow=True, fancybox=True,prop={'size': 30})
    plt.ylabel('Measurement')
    plt.show()

I can't see strong difference between 0 and 1 label.

4. Features vs Features¶

We will want to know if the features are correlated, so for each class we'll calculate the correlation matrix between all of the 512 features, and print the highest values and the correlation matrix. By doing it, we can find more patterns in the data, maybe to collapse few consecutive measurements into one value or remove some correlated features.

Correlation matrix for both classes¶

Results:¶

0.3 is the highest correlation value. It is not that high correlation between features

import matplotlib.pyplot as plt
import seaborn as sns

corr = train_data.iloc[:,range(512)].corr()
flat=corr.as_matrix().flatten()
print("Highest feature correlation is: ",np.sort(flat)[-513]) # There are 512 1's because each feature is correlated with itself.

# if you want to print correlation matrix undomment this:
figure(figsize=(30,20))
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)
plt.show()

Highest feature correlation is:  0.3106630831281946

Correlation matrix for Earthquake class (label 1)¶

It'll probably be more meaningfull to see the correlation between features for each of the classes. So we'll start with label 1(Earthquake).

Results:¶

0.6 is the highest correlation value in the matrix. It is pretty high correlation, we will print the correlation matrix and look for patterns

one_label_count_train = train_data.loc[train_data[512] == 1]
corr = one_label_count_train.iloc[:,range(512)].corr()
flat=corr.as_matrix().flatten()
print("Highest feature correlation is: ",np.sort(flat)[-513]) # There are 512 1's because each feature is correlated with itself.

# if you want to print correlation matrix undomment this:
figure(figsize=(30,20))
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)
plt.show()

Highest feature correlation is:  0.6581790966916521

Correlation matrix for No-Earthquake class (label 0)¶

Results:¶

0.3 is the highest value in the correlation matrix. It isn't a high correlation.

zero_label_count_train = train_data.loc[train_data[512] == 0]
corr = zero_label_count_train.iloc[:,range(512)].corr()
flat=corr.as_matrix().flatten()
print("Highest feature correlation is: ",np.sort(flat)[-513]) # There are 512 1's because each feature is correlated with itself.

# if you want to print correlation matrix undomment this:
figure(figsize=(30,20))
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)
plt.show()

Highest feature correlation is:  0.322667026997916

Correlation matrices conclusions:¶

There are many features that are correlated in the Earthquake class. We need to look into these features. Maybe if we will remove some correlated features, we will get better classification results.

5. Features, describe yourselfs!¶

To better understand the features, we will print a boxplot for each feature. This way we can see if the data is corrupted with noisy measurements, understand the scale of each feature and identify outliers.

Box plot is the very meaningful representation of data.
It's great way to see how every feature "behaves". For example, we can see the extreme values and identify if some of the data is corrupted (too high or too low measurements).
In addition, it is a good starting position for outliers detection. (everything higher than 1.5*IQR start to be suspicious as an outlier, but we need further gudge some of the cases)

For better visibility, for each 50 features we will print two plots. First contains boxplots for each feature. The second containes a single boxplot represent statistics about outliers from these 50 features. X - feature number, Y - Measurement value, Circles - outliers.

Same as in correlation matrices, first we will print plots for both of the classes together, then Earthquake class then No-Earthquake class¶

Both classes features boxplots¶

Results:¶

We can see that 76,339, 368 features has some wider range of boxplot.
Most of the features behave pretty much the same - This means it's a stationary data which is good for normality assumptions of many ML algorithms.
The average amount of outliers is around 50 all over the features. It's a bit high if we consider that our train set size is ~300 samples.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def features_boxplots(data):
    for i in range(0,500,50):
        cols_range = range(i,i+50)
        fig, ax = plt.subplots(figsize=(20,  10))
        _, bp = data.iloc[:,cols_range].boxplot(list(cols_range),return_type='both')
        plt.title("%d to %d features"%(i,i+50), fontdict=font)
        plt.xlabel('Feature', fontdict=font)
        plt.ylabel('Measurement', fontdict=font)
        plt.show()

        # print boxplot of outliers
        outliers = [flier.get_ydata() for flier in bp["fliers"]]
        plt.boxplot([len(li) for li in outliers])
        plt.title("%d to %d outliers"%(i,i+50), fontdict=font)
    #     plt.xlabel('outliers', fontdict=font)
        plt.ylabel('Count', fontdict=font)
        plt.show()


    cols_range = range(500,512)
    fig, ax = plt.subplots(figsize=(20,  10))
    _, bp = data.iloc[:,cols_range].boxplot(list(cols_range),return_type='both')
    plt.title("%d to %d"%(500,512), fontdict=font)
    plt.xlabel('Feature', fontdict=font)
    plt.ylabel('Measurement', fontdict=font)
    plt.show()

    # print boxplot of outliers
    outliers = [flier.get_ydata() for flier in bp["fliers"]]
    plt.boxplot([len(li) for li in outliers])
    plt.title("%d to %d outliers"%(i,i+50), fontdict=font)
    #     plt.xlabel('outliers', fontdict=font)
    plt.ylabel('Count', fontdict=font)
    plt.show()
    
    
features_boxplots(train_data)

Label 0(No-Earthquake) features boxplots¶

Results:¶

Almost the exact same results as for both of the classes. Make sense because the data is imbalanced and label 0 is the larger class, so it has more weight in the results.
The average number of outliers is a bit lower here, its around 40-45 outliers per feature. It's 1:5 ratio of outlier to number of samples with label 0 (264 samples).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

zero_label_count_train = train_data.loc[train_data[512] == 0]
features_boxplots(zero_label_count_train)

Label 1 (Earthquake) features boxplot¶

Results¶

We can see that there are much more features with wide boxplot. It may be due to a small sample and not because of strong differences between the classes.
The average number of outliers is 10, it's 1:5 ratio of outliers to number of label 1 samples (~58 samples).

ones_label_count_train = train_data.loc[train_data[512] == 1]
features_boxplots(ones_label_count_train)

Final conclusions about features boxplot¶

We can see that the data is stationary (the means and stds are the same for most of the features).
We can see that there is a lot of outliers. (average of ~50 outliers out of ~300 in train set is pretty high)
We aren't domain experts, so we are not sure what is the best way to treat them.
Maybe we can try to see what happens when we remove,cap(all above\below some max\min value are the same),transform them.
We can see that there are no extreme cases (like measurement with a value of more than 20), means our data is not corrupted and there is no problem with the sensors.
We can see that some of the features have wider boxplot (like feature 76). Maybe it can become important.

6. Can we finally start playing with ML???¶

Now when we have better understanding of the data, we will apply some out of the box machine learning algorithms.

Split to features and labels¶

X_train = train_data.iloc[:,range(0,512)]
y_train = train_data.iloc[:,512]

X_test = test_data.iloc[:,range(0,512)]
y_test = test_data.iloc[:,512]

Try out of the box ML models¶

I'll apply some ML classifiers from sklearn python library for ML.
All of them are set with default parameters, we just want to feel the results.

The below code will iterate over some classifiers, fit on train set, predict on test set. Then it will print all the measurements (accuracy, precision, recall, f1), and the confusion matrix for each classifier.
With the confusion matrix its easy to see the mistakes the model make on each class.

In our case, because the classes are imbalanced, it will not be useful to look only for high accuracy. This way, the model can learn to predict one class, and will get good accuracy results but will ignore predicting the other class. This is not a good classifier.

In our case, I'm not sure if we would like our model to:

1. Predict earthquakes with high precision, means to correctly predict all the earthquakes. This can result high false positive predictions. (Imagine every prediction will result the evacuation of all the people in the building)
2. When predicting an earthquake, it will be correct with high probability.  We wouldn't want many False-negative

F1 measure is a weighted average of both precision and recall. In our case, it will be more meaningful than accuracy.

Important note:
We will not finelize a specific model. We will not hypertune the parameters using cross validation. We only want to feel some out of the box classifiers result on the data and analyze the results.

Results:¶

We can see that some of the models has very high recall but very low precision (RBF SVM, always predict 0) (make sense, the classes are imbalanced and some of the models get good results by only predicting one class).

AdaBoost is the best out of the box classifier. All of it's measures are the highest 0.72 precision, 0.76 recall, 0.72 f1 and 0.75 acc. In addition, it's doesn't predict only one class.
0.75% is also the highest accuracy on this test set by the website we linked in the top of this notebook.

We can see that Gaussian Process has very similar scores as Adaboost. It has 0.7482 acc, 0.71 precision, 0.75 recall and 0.71 f1. And also doesn't predict one class.

# I'm ignoring warnings to make things to be printed more nicely.
# I read all the warnings, all of them doesn't harm the results. just sklearn notifications.
import warnings
warnings.filterwarnings('ignore')  # "error", "ignore", "always", "default", "module" or "once"

print(__doc__)


import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.metrics import confusion_matrix, accuracy_score,classification_report


# Some helper function to nicly print confusion matrixes
def plot_confusion_matrix(cm, classes,
                              normalize=False,
                              title='Confusion matrix',
                              cmap=plt.cm.Blues):
        """
        This function prints and plots the confusion matrix.
        Normalization can be applied by setting `normalize=True`.
        """
        if normalize:
            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
            print("Normalized confusion matrix")
        else:
            print('Confusion matrix, without normalization')

        print(cm)

        plt.imshow(cm, interpolation='nearest', cmap=cmap)
        plt.title(title)
        plt.colorbar()
        tick_marks = np.arange(len(classes))
        plt.xticks(tick_marks, classes, rotation=45)
        plt.yticks(tick_marks, classes)

        fmt = '.2f' if normalize else 'd'
        thresh = cm.max() / 2.
        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
            plt.text(j, i, format(cm[i, j], fmt),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")

        plt.tight_layout()
        plt.ylabel('True label')
        plt.xlabel('Predicted label')

        
        
def run_sklean_expiraments(X_train,y_train,X_test,y_test,clasifiers_to_remove=[]):
    names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
             "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
             "Naive Bayes", "QDA"]
    names = [x for x in names if x not in clasifiers_to_remove]
    
    classifiers = [
        KNeighborsClassifier(3),
        SVC(kernel="linear", C=0.025),
        SVC(gamma=2, C=1),
        GaussianProcessClassifier(1.0 * RBF(1.0)),
        DecisionTreeClassifier(max_depth=5),
        RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
        MLPClassifier(alpha=1),
        AdaBoostClassifier(),
        GaussianNB(),
        QuadraticDiscriminantAnalysis()]


    # iterate over classifiers
    for name, clf in zip(names, classifiers):
            y_pred = clf.fit(X_train, y_train).predict(X_test)
            score = clf.score(X_test, y_test)
            print("#"*30," Classifier: ",name,", Accuracy: ",score, "#"*30)
            print(classification_report(y_test,y_pred))

            # Compute confusion matrix
            cnf_matrix = confusion_matrix(y_test, y_pred)
            np.set_printoptions(precision=2)

            # Plot non-normalized confusion matrix
            plt.figure()
            plot_confusion_matrix(cnf_matrix, classes=[0,1],
                                  title='Confusion matrix, without normalization')

            # Plot normalized confusion matrix
            plt.figure()
            plot_confusion_matrix(cnf_matrix, classes=[0,1], normalize=True,
                                  title='Normalized confusion matrix')

            plt.show()
            
run_sklean_expiraments(X_train,y_train,X_test,y_test)

Automatically created module for IPython interactive environment
##############################  Classifier:  Nearest Neighbors , Accuracy:  0.7338129496402878 ##############################
             precision    recall  f1-score   support

          0       0.75      0.97      0.85       104
          1       0.25      0.03      0.05        35

avg / total       0.62      0.73      0.65       139

Confusion matrix, without normalization
[[101   3]
 [ 34   1]]
Normalized confusion matrix
[[0.97 0.03]
 [0.97 0.03]]

##############################  Classifier:  Linear SVM , Accuracy:  0.6402877697841727 ##############################
             precision    recall  f1-score   support

          0       0.75      0.79      0.77       104
          1       0.24      0.20      0.22        35

avg / total       0.62      0.64      0.63       139

Confusion matrix, without normalization
[[82 22]
 [28  7]]
Normalized confusion matrix
[[0.79 0.21]
 [0.8  0.2 ]]

##############################  Classifier:  RBF SVM , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

##############################  Classifier:  Gaussian Process , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.78      0.92      0.85       104
          1       0.50      0.23      0.31        35

avg / total       0.71      0.75      0.71       139

Confusion matrix, without normalization
[[96  8]
 [27  8]]
Normalized confusion matrix
[[0.92 0.08]
 [0.77 0.23]]

##############################  Classifier:  Decision Tree , Accuracy:  0.6834532374100719 ##############################
             precision    recall  f1-score   support

          0       0.75      0.86      0.80       104
          1       0.29      0.17      0.21        35

avg / total       0.64      0.68      0.65       139

Confusion matrix, without normalization
[[89 15]
 [29  6]]
Normalized confusion matrix
[[0.86 0.14]
 [0.83 0.17]]

##############################  Classifier:  Random Forest , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

##############################  Classifier:  Neural Net , Accuracy:  0.7122302158273381 ##############################
             precision    recall  f1-score   support

          0       0.76      0.89      0.82       104
          1       0.35      0.17      0.23        35

avg / total       0.66      0.71      0.67       139

Confusion matrix, without normalization
[[93 11]
 [29  6]]
Normalized confusion matrix
[[0.89 0.11]
 [0.83 0.17]]

##############################  Classifier:  AdaBoost , Accuracy:  0.7553956834532374 ##############################
             precision    recall  f1-score   support

          0       0.79      0.92      0.85       104
          1       0.53      0.26      0.35        35

avg / total       0.72      0.76      0.72       139

Confusion matrix, without normalization
[[96  8]
 [26  9]]
Normalized confusion matrix
[[0.92 0.08]
 [0.74 0.26]]

##############################  Classifier:  Naive Bayes , Accuracy:  0.6762589928057554 ##############################
             precision    recall  f1-score   support

          0       0.77      0.81      0.79       104
          1       0.33      0.29      0.31        35

avg / total       0.66      0.68      0.67       139

Confusion matrix, without normalization
[[84 20]
 [25 10]]
Normalized confusion matrix
[[0.81 0.19]
 [0.71 0.29]]

##############################  Classifier:  QDA , Accuracy:  0.3237410071942446 ##############################
             precision    recall  f1-score   support

          0       0.68      0.18      0.29       104
          1       0.23      0.74      0.36        35

avg / total       0.57      0.32      0.31       139

Confusion matrix, without normalization
[[19 85]
 [ 9 26]]
Normalized confusion matrix
[[0.18 0.82]
 [0.26 0.74]]

7. It's all about perspective¶

After investigating the ML results, we will try to improve them by applying transformations (Standardize features, Furie transformation) on the data and apply the previous ML algorithms again and investigate the results.

Transformation - Standardize features¶

Even though all our measurement are in log scale, we will try to apply standardization on the measurements and then apply the classifiers. Standardize features by removing the mean and scaling to unit variance

Important: When doing standardization on the values, we will learn the standard distribution of all the features in train, and then transform the train and the test by the same scale. This way we aren't cheating by knowing the test distribution and scale.

Results:¶

We can see that almost all classifiers got better scores. It actually helped!

For example:

1. Nearest Neighbors got from 69% accuracy to 73%.
2. Gaussian Process from 71% to 74.82% without predicting only one class (74.82% is usually the accuracy for predicting one class, like RBF SVM)

We still couldn't pass 75% accuracy, and still the highest precision, recall, and f1 are the same as without transformation and still belong to Adagrad classifier.

We can notice that some classifiers always predict 0. Probably because most of the labels are 0.

import numpy as np
from sklearn.preprocessing import StandardScaler

# Standartization
X_scaler = StandardScaler()
X_train = X_scaler.fit_transform(train_data.iloc[:,range(0,512)].values)
y_train = train_data.iloc[:,512]

X_test = X_scaler.transform(test_data.iloc[:,range(0,512)].values)
y_test = test_data.iloc[:,512]

run_sklean_expiraments(X_train,y_train,X_test,y_test)

##############################  Classifier:  Nearest Neighbors , Accuracy:  0.7338129496402878 ##############################
             precision    recall  f1-score   support

          0       0.75      0.97      0.85       104
          1       0.25      0.03      0.05        35

avg / total       0.62      0.73      0.65       139

Confusion matrix, without normalization
[[101   3]
 [ 34   1]]
Normalized confusion matrix
[[0.97 0.03]
 [0.97 0.03]]

##############################  Classifier:  Linear SVM , Accuracy:  0.6402877697841727 ##############################
             precision    recall  f1-score   support

          0       0.75      0.79      0.77       104
          1       0.24      0.20      0.22        35

avg / total       0.62      0.64      0.63       139

Confusion matrix, without normalization
[[82 22]
 [28  7]]
Normalized confusion matrix
[[0.79 0.21]
 [0.8  0.2 ]]

##############################  Classifier:  RBF SVM , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

##############################  Classifier:  Gaussian Process , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.78      0.92      0.85       104
          1       0.50      0.23      0.31        35

avg / total       0.71      0.75      0.71       139

Confusion matrix, without normalization
[[96  8]
 [27  8]]
Normalized confusion matrix
[[0.92 0.08]
 [0.77 0.23]]

##############################  Classifier:  Decision Tree , Accuracy:  0.697841726618705 ##############################
             precision    recall  f1-score   support

          0       0.76      0.87      0.81       104
          1       0.33      0.20      0.25        35

avg / total       0.65      0.70      0.67       139

Confusion matrix, without normalization
[[90 14]
 [28  7]]
Normalized confusion matrix
[[0.87 0.13]
 [0.8  0.2 ]]

##############################  Classifier:  Random Forest , Accuracy:  0.7410071942446043 ##############################
             precision    recall  f1-score   support

          0       0.75      0.99      0.85       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.74      0.64       139

Confusion matrix, without normalization
[[103   1]
 [ 35   0]]
Normalized confusion matrix
[[0.99 0.01]
 [1.   0.  ]]

##############################  Classifier:  Neural Net , Accuracy:  0.7050359712230215 ##############################
             precision    recall  f1-score   support

          0       0.76      0.88      0.82       104
          1       0.33      0.17      0.23        35

avg / total       0.65      0.71      0.67       139

Confusion matrix, without normalization
[[92 12]
 [29  6]]
Normalized confusion matrix
[[0.88 0.12]
 [0.83 0.17]]

##############################  Classifier:  AdaBoost , Accuracy:  0.7553956834532374 ##############################
             precision    recall  f1-score   support

          0       0.79      0.92      0.85       104
          1       0.53      0.26      0.35        35

avg / total       0.72      0.76      0.72       139

Confusion matrix, without normalization
[[96  8]
 [26  9]]
Normalized confusion matrix
[[0.92 0.08]
 [0.74 0.26]]

##############################  Classifier:  Naive Bayes , Accuracy:  0.6762589928057554 ##############################
             precision    recall  f1-score   support

          0       0.77      0.81      0.79       104
          1       0.33      0.29      0.31        35

avg / total       0.66      0.68      0.67       139

Confusion matrix, without normalization
[[84 20]
 [25 10]]
Normalized confusion matrix
[[0.81 0.19]
 [0.71 0.29]]

##############################  Classifier:  QDA , Accuracy:  0.3237410071942446 ##############################
             precision    recall  f1-score   support

          0       0.68      0.18      0.29       104
          1       0.23      0.74      0.36        35

avg / total       0.57      0.32      0.31       139

Confusion matrix, without normalization
[[19 85]
 [ 9 26]]
Normalized confusion matrix
[[0.18 0.82]
 [0.26 0.74]]

Transformation - Fourier transformation¶

In signal processing, the Fourier transform often takes a time series or a function of continuous time, and maps it into a frequency spectrum. That is, it takes a function from the time domain into the frequency domain. Our features are measurements over hours, so I thought it would be nice to try. I got the idea from a guy that won Kaggle competition on time series data and advised to try this transformation.

Some of the classifiers had some problems with the transformations, so I ignored them.

Results:¶

We can see that some of the classifies like Nearest Neighbors got boost in accuracy (69% to 71%), but most of the classifiers predicted only one class which is bad.

Not that good transformation.

import numpy as np
from sklearn.preprocessing import StandardScaler
from scipy import fftpack


X_train = train_data.iloc[:,range(0,512)].values
X_test = test_data.iloc[:,range(0,512)].values

# Fourier transform
X_train = fftpack.fft(X_train)
y_train = train_data.iloc[:,512]

X_test = fftpack.fft(X_test)
y_test = test_data.iloc[:,512]

run_sklean_expiraments(X_train,y_train,X_test,y_test,clasifiers_to_remove=["Neural Net", "AdaBoost","Naive Bayes", "QDA"])

##############################  Classifier:  Nearest Neighbors , Accuracy:  0.7194244604316546 ##############################
             precision    recall  f1-score   support

          0       0.74      0.96      0.84       104
          1       0.00      0.00      0.00        35

avg / total       0.55      0.72      0.63       139

Confusion matrix, without normalization
[[100   4]
 [ 35   0]]
Normalized confusion matrix
[[0.96 0.04]
 [1.   0.  ]]

##############################  Classifier:  Linear SVM , Accuracy:  0.6115107913669064 ##############################
             precision    recall  f1-score   support

          0       0.75      0.73      0.74       104
          1       0.24      0.26      0.25        35

avg / total       0.62      0.61      0.62       139

Confusion matrix, without normalization
[[76 28]
 [26  9]]
Normalized confusion matrix
[[0.73 0.27]
 [0.74 0.26]]

##############################  Classifier:  RBF SVM , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

##############################  Classifier:  Gaussian Process , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

##############################  Classifier:  Decision Tree , Accuracy:  0.7050359712230215 ##############################
             precision    recall  f1-score   support

          0       0.74      0.94      0.83       104
          1       0.00      0.00      0.00        35

avg / total       0.55      0.71      0.62       139

Confusion matrix, without normalization
[[98  6]
 [35  0]]
Normalized confusion matrix
[[0.94 0.06]
 [1.   0.  ]]

##############################  Classifier:  Random Forest , Accuracy:  0.7482014388489209 ##############################
             precision    recall  f1-score   support

          0       0.75      1.00      0.86       104
          1       0.00      0.00      0.00        35

avg / total       0.56      0.75      0.64       139

Confusion matrix, without normalization
[[104   0]
 [ 35   0]]
Normalized confusion matrix
[[1. 0.]
 [1. 0.]]

8. Now some deep learning (LSTM) for the fun¶

We will use LSTM NN because our data is time series.

I tried many architectures, many hyperparameters. This is the end result, but much more work can be done to optimize it.

Results:¶

The network isn't that good, it overfit the train sample, and always predict one class. I tried to avoid it with many techniques:

1. Reduce the architecture complexity - Force it to learn with minimal conditions(weights).
2. Dropout - Randomly ignore some of the weights. It reduce overfitting.
3. Different weight initialization techniques - Help with vanishing gradients.
4. Different losses and optimizers - Skip saddle and local minimum points.
5. weight classes - Deal with class imbalance.

Nothing really worked. Maybe the data is too small.

Prepare the dataset for LSTM network

import numpy as np
X_train = train_data.iloc[:,range(0,512)]
y_train = train_data.iloc[:,512]

X_test = test_data.iloc[:,range(0,512)]
y_test = test_data.iloc[:,512]

keras_X_train = np.reshape(X_train.as_matrix(),(X_train.shape[0],1,X_train.shape[1]))
keras_X_test = np.reshape(X_test.as_matrix(),(X_test.shape[0],1,X_test.shape[1]))

import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import keras
from keras.optimizers import SGD,Adam


epochs=60
batch_size=32
dropout = 0.4
recurrent_dropout=0.4
# create and fit the LSTM network
look_back = 512
model = Sequential()
model.add(LSTM(4, activation='relu', input_shape=(1, look_back),
               kernel_initializer='random_normal',
               return_sequences=True,
               dropout=dropout,
               recurrent_dropout=recurrent_dropout))
model.add(LSTM(3,  activation='relu'))
model.add(Dense(1,activation='sigmoid'))


optimizer = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
# optimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00001, amsgrad=False)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

early_stopping = keras.callbacks.EarlyStopping(monitor='val_acc',
                              patience=10,
                              verbose=0, mode='auto')
model.summary()
class_weight = {0: 5.,
                1: 1.}

model.fit(keras_X_train, y_train, 
          validation_split = 0.2,
          epochs=epochs,
          batch_size=batch_size,
          verbose=1,
          class_weight=class_weight,
          callbacks=[early_stopping])

score, acc = model.evaluate(keras_X_test, y_test, batch_size=1)
print('Test score:', score)
print('Test accuracy:', acc)

# Test to see if the model predict any 1 class cases
rounded_predictions = model.predict_classes(keras_X_test, batch_size=1,verbose=1)
[i for i in rounded_predictions if i==1]

Using TensorFlow backend.

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_1 (LSTM)                (None, 1, 4)              8272      
_________________________________________________________________
lstm_2 (LSTM)                (None, 3)                 96        
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 4         
=================================================================
Total params: 8,372
Trainable params: 8,372
Non-trainable params: 0
_________________________________________________________________
Train on 257 samples, validate on 65 samples
Epoch 1/60
257/257 [==============================] - 14s 56ms/step - loss: 1.5631 - acc: 0.8093 - val_loss: 0.6313 - val_acc: 0.8615
Epoch 2/60
257/257 [==============================] - 0s 2ms/step - loss: 0.9453 - acc: 0.8093 - val_loss: 0.7582 - val_acc: 0.8615
Epoch 3/60
257/257 [==============================] - 0s 2ms/step - loss: 0.9733 - acc: 0.8093 - val_loss: 0.6493 - val_acc: 0.8615
Epoch 4/60
257/257 [==============================] - 0s 2ms/step - loss: 0.8254 - acc: 0.8093 - val_loss: 0.6181 - val_acc: 0.8615
Epoch 5/60
257/257 [==============================] - 0s 2ms/step - loss: 0.7696 - acc: 0.8093 - val_loss: 0.6408 - val_acc: 0.8615
Epoch 6/60
257/257 [==============================] - 0s 2ms/step - loss: 0.7638 - acc: 0.8093 - val_loss: 0.6282 - val_acc: 0.8615
Epoch 7/60
257/257 [==============================] - 0s 2ms/step - loss: 0.7549 - acc: 0.8093 - val_loss: 0.6163 - val_acc: 0.8615
Epoch 8/60
257/257 [==============================] - 0s 2ms/step - loss: 0.7415 - acc: 0.8093 - val_loss: 0.6115 - val_acc: 0.8615
Epoch 9/60
257/257 [==============================] - 0s 1ms/step - loss: 0.7222 - acc: 0.8093 - val_loss: 0.6096 - val_acc: 0.8615
Epoch 10/60
257/257 [==============================] - 0s 1ms/step - loss: 0.6892 - acc: 0.8093 - val_loss: 0.6104 - val_acc: 0.8615
Epoch 11/60
257/257 [==============================] - 0s 2ms/step - loss: 0.6373 - acc: 0.8093 - val_loss: 0.6186 - val_acc: 0.8615
139/139 [==============================] - 1s 10ms/step
Test score: 0.8806720155616793
Test accuracy: 0.7482014388489209
139/139 [==============================] - 1s 11ms/step

[]

We can see that the network not exactly predict one class. It predicts 2 probabilities , one for each class, and the class prediction phase take the higher probabilities with some threshold.

We can play with the predictions threshold. The sigmoid function in the last layer give us a number between 0 and 1 for a class (and 1- this number for the second class). We can pick the threshold that best balance precision and recall.

In the code below, we will use our LSTM model to predict on test set.
Then for each test sample, we will print the prediction's probabilitie with the right label. (this way we can look for the right threshold)
Then we will pick some threshold and see the new accuracy.

import sklearn

# Predict on test
rounded_predictions = model.predict(keras_X_test, batch_size=1,verbose=1)

# Print predictions values
for i in range(len(rounded_predictions)):
    print(rounded_predictions[i],y_test[i])
    
# Set new threshold and check the new accuracy
new = [0 if x < 0.08 else 1 for x in rounded_predictions]
print("new threshold predictions:")
print(new)
print(sklearn.metrics.accuracy_score(y_test,new))

139/139 [==============================] - 29s 210ms/step
[0.01] 0
[0.09] 1
[0.02] 0
[0.09] 0
[0.08] 0
[0.] 0
[0.] 0
[0.01] 0
[0.06] 1
[0.01] 0
[0.01] 0
[0.08] 0
[0.07] 0
[0.08] 0
[0.06] 0
[0.06] 1
[0.08] 0
[0.06] 0
[0.07] 0
[0.06] 0
[0.07] 1
[0.02] 0
[0.08] 0
[0.06] 0
[0.09] 1
[0.08] 1
[0.09] 0
[0.02] 0
[0.03] 0
[0.08] 0
[0.06] 1
[0.07] 0
[0.08] 0
[0.07] 0
[0.06] 0
[0.09] 0
[0.01] 0
[0.07] 0
[0.01] 0
[0.08] 0
[0.01] 0
[0.08] 0
[0.05] 1
[0.09] 1
[0.05] 1
[0.03] 1
[0.03] 1
[0.04] 0
[0.05] 0
[0.03] 0
[0.04] 1
[0.01] 0
[0.01] 0
[0.07] 0
[0.06] 0
[0.08] 0
[0.05] 1
[0.05] 1
[0.06] 0
[0.09] 0
[0.01] 0
[0.04] 0
[0.09] 0
[0.07] 1
[0.08] 0
[0.07] 0
[0.08] 0
[0.08] 0
[0.09] 0
[0.06] 0
[0.08] 0
[0.] 1
[0.07] 0
[0.07] 1
[0.08] 0
[0.05] 0
[0.07] 0
[0.04] 0
[0.08] 1
[0.05] 1
[0.07] 1
[0.05] 0
[0.02] 0
[0.06] 0
[0.08] 0
[0.04] 0
[0.03] 0
[0.02] 0
[0.07] 1
[0.01] 0
[0.06] 0
[0.05] 0
[0.09] 0
[0.08] 0
[0.] 0
[0.06] 0
[0.01] 0
[0.08] 0
[0.02] 1
[0.06] 0
[0.08] 0
[0.07] 0
[0.08] 0
[0.04] 0
[0.08] 1
[0.08] 0
[0.07] 0
[0.08] 1
[0.08] 0
[0.07] 0
[0.04] 1
[0.08] 0
[0.05] 1
[0.05] 0
[0.08] 0
[0.08] 0
[0.09] 0
[0.07] 0
[0.03] 0
[0.09] 0
[0.08] 0
[0.03] 1
[0.04] 0
[1.49e-05] 0
[0.] 1
[0.08] 1
[0.08] 0
[0.07] 1
[0.06] 0
[0.03] 0
[0.08] 0
[0.08] 1
[0.] 1
[0.08] 0
[0.03] 1
[0.03] 0
[0.08] 1
[0.09] 0
[0.06] 0
new threshold predictions:
[0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0]
0.5971223021582733

We can also look at the ROC curve and see the precision, recall balance. Here we plot the ROC curve of our LSTM model and Random Forest model for comparison.

from sklearn.metrics import roc_curve
y_pred_keras = model.predict(keras_X_test).ravel()
fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)

from sklearn.metrics import auc
auc_keras = auc(fpr_keras, tpr_keras)


# Comparison
from sklearn.ensemble import RandomForestClassifier
# Supervised transformation based on random forests
rf = RandomForestClassifier(max_depth=3, n_estimators=10)
rf.fit(X_train, y_train)

y_pred_rf = rf.predict_proba(X_test)[:, 1]
fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_pred_rf)
auc_rf = auc(fpr_rf, tpr_rf)


plt.figure(1)
figure(figsize=(20,10))
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))
plt.xlabel('False positive rate',fontdict=font)
plt.ylabel('True positive rate',fontdict=font)
plt.title('ROC curve')
plt.legend(loc='best',prop={'size': 30})
plt.show()
# # Zoom in view of the upper left corner.
# plt.figure(2)
# plt.xlim(0, 0.2)
# plt.ylim(0.8, 1)
# plt.plot([0, 1], [0, 1], 'k--')
# plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))
# plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))
# plt.xlabel('False positive rate')
# plt.ylabel('True positive rate')
# plt.title('ROC curve (zoomed in at top left)')
# plt.legend(loc='best')
# plt.show()

<matplotlib.figure.Figure at 0x1da04d80cf8>

Not that promising. Even out of the box random forest beats our LSTM.
We can see that both algorithms couldn't achieve higher TPR and low FPR at the same time.

9. Run all combinations¶

Auto-Sklearn is a module that try to search for an ensemble of classifiers from sklearn library. I run it for 24 hours.

Results:¶

Accuracy of 0.755. We got it using Adagrad. I'm sorry, I run this algorithm on other computer, and didn't save the model. So all you can see is the final accuracy, without the other measurements.

import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics


cls = autosklearn.classification.AutoSklearnClassifier()
cls.fit(X_train, y_train)
predictions = cls.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)

[WARNING] [2018-08-01 13:38:38,745:EnsembleBuilder(1):fda9f17a2669a28d2b714bcad826786e] No models better than random - using Dummy Classifier!
[WARNING] [2018-08-01 13:38:38,756:EnsembleBuilder(1):fda9f17a2669a28d2b714bcad826786e] No models better than random - using Dummy Classifier!

/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)

[WARNING] [2018-08-01 13:49:56,023:smac.intensification.intensification.Intensifier] Challenger was the same as the current incumbent; Skipping challenger
[WARNING] [2018-08-01 13:49:56,023:smac.intensification.intensification.Intensifier] Challenger was the same as the current incumbent; Skipping challenger

/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
/home/gelleral/anaconda3/envs/shay_temp/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)

Accuracy score 0.7553956834532374

10. If I had infinite amount of time - Future work¶

I have lots of ideas to try:

The classes are imbalances, maybe we can try to upsample from the smaller class(take the same sample multiple times) or downsample from the bigger class (ignore some of the samples in the bigger class).
I would try to generate more instances with SMOTE or similar algorithms.
Because of the class imbalance, I would try to learn one-class classifiers on the bigger class and predict on test set with both classes.
The data is time series look stationary. Try sliding window models or other models for stationary data.
We saw that the train and test sets aren't that similar. I would try to mix the train and test sets, shuffle them and split them again.
We saw that there are a lot of outliers. I would try to remove, cap(pick max\min values and all above\below values will be transformed to that min\max values), transform them and see the results.
We know that the Richter Scale is logarithmic. We can upscale the values of all the measurements and then apply the classifiers.
Try some embedding methods (like autoencoders) to transform the features into another hidden representation.
From the correlation matrices conclusions, I would remove some label 0 correlated features and then apply the classifiers.
Hypertune the best models parameters.
Learn the robustness of the models - Perform k-fold cross validation or multiple train\test splits to find the margins of the model's scores. At the end take the algorithms with best margin and scores and finalize it by learning on all over the data. It will be ready to predict on new unseen data.

	0	1	2	3	4	5	6	7	8	9	...	503	504	505	506	507	508	509	510	511	512
0	-0.51801	-0.51801	2.65420	-0.51801	-0.51801	-0.51801	-0.51801	1.45620	2.55840	-0.51801	...	-0.51801	-0.51801	-0.51801	-0.51801	-0.51801	-0.51801	-0.51801	1.46580	-0.51801	1
1	1.94370	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	...	3.36560	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	-0.35311	0
2	2.63850	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	...	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	-0.31610	0
3	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	...	-0.53114	2.14740	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	-0.53114	0
4	-0.59366	2.02010	1.17470	-0.59366	-0.59366	1.60600	1.21790	1.58880	-0.59366	-0.59366	...	-0.59366	-0.59366	-0.59366	1.49390	-0.59366	-0.59366	-0.59366	1.89930	-0.59366	1
5	4.72670	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	...	-0.24025	-0.24025	-0.24025	3.88790	-0.24025	-0.24025	-0.24025	-0.24025	-0.24025	0
6	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	...	-0.42951	2.28960	-0.42951	2.31050	-0.42951	-0.42951	-0.42951	-0.42951	-0.42951	0
7	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	2.31530	-0.55077	-0.55077	-0.55077	...	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	-0.55077	0
8	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	...	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	-0.20715	0
9	1.47920	-0.52883	-0.52883	1.49770	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	...	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	-0.52883	0
10	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	...	-0.44799	1.99530	2.31310	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	-0.44799	0
11	-0.68904	1.13300	1.17400	2.10140	-0.68904	1.34640	-0.68904	-0.68904	0.96062	-0.68904	...	1.67460	1.29710	-0.68904	-0.68904	-0.68904	0.96882	-0.68904	-0.68904	-0.68904	0
12	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	...	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	-0.20405	3.87950	-0.20405	0
13	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	...	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	-0.26656	0
14	-0.59892	-0.59892	-0.59892	-0.59892	1.47650	1.52810	-0.59892	1.17510	-0.59892	-0.59892	...	1.83810	-0.59892	-0.59892	1.64010	1.81230	-0.59892	-0.59892	-0.59892	-0.59892	0
15	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	2.48920	2.60010	...	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	-0.38480	0
16	1.13830	-0.60440	1.60130	1.74440	1.18040	1.09620	-0.60440	-0.60440	-0.60440	-0.60440	...	-0.60440	1.75280	-0.60440	-0.60440	-0.60440	-0.60440	-0.60440	-0.60440	-0.60440	0
17	-0.30836	-0.30836	-0.30836	-0.30836	3.79720	-0.30836	2.95110	-0.30836	-0.30836	-0.30836	...	3.13140	-0.30836	2.65980	-0.30836	-0.30836	-0.30836	-0.30836	-0.30836	-0.30836	0
18	3.22480	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	2.35760	-0.41310	2.06150	-0.41310	...	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	-0.41310	0
19	1.76300	-0.47368	2.46440	-0.47368	-0.47368	-0.47368	-0.47368	-0.47368	1.86460	-0.47368	...	1.82400	-0.47368	1.71210	-0.47368	-0.47368	-0.47368	-0.47368	-0.47368	-0.47368	0
20	-0.43394	-0.43394	-0.43394	2.12390	2.09200	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	...	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	-0.43394	0
21	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	3.79380	-0.25160	-0.25160	-0.25160	-0.25160	...	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	-0.25160	0
22	-0.48066	-0.48066	-0.48066	-0.48066	1.75460	-0.48066	-0.48066	-0.48066	-0.48066	-0.48066	...	3.01930	1.90170	1.85270	-0.48066	-0.48066	-0.48066	-0.48066	-0.48066	-0.48066	1
23	-0.51388	1.49480	-0.51388	-0.51388	-0.51388	-0.51388	-0.51388	-0.51388	-0.51388	1.53290	...	-0.51388	-0.51388	-0.51388	-0.51388	-0.51388	2.32300	-0.51388	-0.51388	-0.51388	0
24	-0.19825	-0.19825	-0.19825	-0.19825	4.88960	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	...	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	-0.19825	1
25	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	...	2.26840	-0.44437	2.60100	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	-0.44437	0
26	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	1.93750	1.64770	-0.47365	-0.47365	...	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	-0.47365	0
27	-0.49262	-0.49262	-0.49262	-0.49262	-0.49262	1.42600	-0.49262	-0.49262	-0.49262	-0.49262	...	3.00850	1.64740	2.50020	1.51620	3.18070	2.97570	2.42640	2.61500	3.23810	1
28	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	...	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	-0.44505	1.77720	-0.44505	-0.44505	1
29	-0.39066	-0.39066	-0.39066	-0.39066	-0.39066	-0.39066	2.07060	-0.39066	-0.39066	-0.39066	...	-0.39066	-0.39066	-0.39066	-0.39066	1.85280	-0.39066	-0.39066	-0.39066	-0.39066	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
292	-0.54807	1.40340	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	...	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	-0.54807	1.58240	1
293	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	...	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	-0.30564	0
294	-0.49722	-0.49722	1.65610	-0.49722	-0.49722	-0.49722	-0.49722	-0.49722	-0.49722	2.14680	...	-0.49722	2.02660	-0.49722	-0.49722	-0.49722	-0.49722	-0.49722	-0.49722	-0.49722	0
295	1.89580	-0.44248	-0.44248	2.06280	-0.44248	-0.44248	-0.44248	-0.44248	-0.44248	-0.44248	...	-0.44248	-0.44248	-0.44248	2.77020	-0.44248	-0.44248	-0.44248	-0.44248	-0.44248	0
296	-0.45192	-0.45192	-0.45192	1.58060	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	...	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	-0.45192	1
297	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	2.96390	-0.31669	-0.31669	...	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	-0.31669	0
298	-0.46453	-0.46453	-0.46453	-0.46453	-0.46453	-0.46453	-0.46453	1.48700	-0.46453	-0.46453	...	-0.46453	-0.46453	-0.46453	1.49650	-0.46453	-0.46453	2.30170	-0.46453	-0.46453	0
299	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	...	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	-0.21338	0
300	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	...	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	-0.34562	0
301	-0.56629	-0.56629	-0.56629	1.65630	1.89410	-0.56629	1.48250	-0.56629	-0.56629	-0.56629	...	1.40930	-0.56629	-0.56629	1.46420	-0.56629	-0.56629	-0.56629	-0.56629	-0.56629	0
302	-0.44351	1.77530	-0.44351	-0.44351	-0.44351	2.12450	1.73420	-0.44351	-0.44351	2.01150	...	-0.44351	-0.44351	-0.44351	-0.44351	-0.44351	-0.44351	-0.44351	-0.44351	-0.44351	0
303	-0.47258	-0.47258	-0.47258	-0.47258	1.73340	-0.47258	-0.47258	-0.47258	-0.47258	2.12220	...	-0.47258	-0.47258	-0.47258	3.22030	-0.47258	-0.47258	-0.47258	1.72370	2.18050	1
304	-0.56541	-0.56541	-0.56541	1.38200	-0.56541	-0.56541	1.78960	-0.56541	-0.56541	-0.56541	...	1.73530	-0.56541	-0.56541	-0.56541	-0.56541	-0.56541	-0.56541	-0.56541	-0.56541	0
305	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	...	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	-0.25173	0
306	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	1.44670	-0.49449	-0.49449	-0.49449	-0.49449	...	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	-0.49449	0
307	-0.54918	1.74860	2.17040	-0.54918	-0.54918	-0.54918	-0.54918	2.17940	1.33570	-0.54918	...	-0.54918	-0.54918	-0.54918	-0.54918	-0.54918	-0.54918	-0.54918	-0.54918	-0.54918	0
308	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	3.31980	-0.26664	-0.26664	-0.26664	...	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	-0.26664	0
309	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	1.52610	1.46650	-0.64389	-0.64389	...	1.46650	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	-0.64389	0
310	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	1.43110	1.33070	-0.55832	-0.55832	-0.55832	...	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	-0.55832	0
311	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	...	1.97400	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	-0.38902	0
312	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	...	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	-0.46793	0
313	-0.54997	-0.54997	-0.54997	-0.54997	-0.54997	2.07620	-0.54997	-0.54997	1.56460	-0.54997	...	-0.54997	-0.54997	-0.54997	1.47080	-0.54997	-0.54997	-0.54997	-0.54997	-0.54997	0
314	-0.56134	-0.56134	-0.56134	2.11420	1.72080	-0.56134	1.65080	-0.56134	1.19610	-0.56134	...	1.61580	-0.56134	1.84320	-0.56134	-0.56134	-0.56134	-0.56134	-0.56134	1.78200	0
315	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	1.99930	2.96460	-0.49659	-0.49659	...	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	-0.49659	0
316	-0.43616	-0.43616	2.71900	-0.43616	-0.43616	-0.43616	-0.43616	2.18220	-0.43616	-0.43616	...	2.65320	-0.43616	-0.43616	3.39820	-0.43616	-0.43616	-0.43616	-0.43616	-0.43616	0
317	-0.57961	-0.57961	-0.57961	-0.57961	1.58450	1.89240	-0.57961	-0.57961	-0.57961	-0.57961	...	3.29110	1.21500	-0.57961	1.70760	1.60210	-0.57961	-0.57961	-0.57961	-0.57961	1
318	-0.47898	-0.47898	1.66500	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	...	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	-0.47898	0
319	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	...	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	-0.26465	0
320	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	...	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	-0.49083	0
321	-0.48392	-0.48392	-0.48392	1.63260	1.65150	-0.48392	-0.48392	-0.48392	-0.48392	2.37910	...	-0.48392	-0.48392	-0.48392	-0.48392	-0.48392	-0.48392	-0.48392	-0.48392	-0.48392	0

	0	1	2	3	4	5	6	7	8	9	...	503	504	505	506	507	508	509	510	511	512
count	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	...	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000	322.000000
mean	0.054087	-0.012574	0.060070	0.063549	-0.008471	0.000617	0.037938	-0.012117	0.028848	-0.061057	...	-0.013927	-0.053676	-0.090294	-0.093705	-0.195098	-0.179217	-0.213229	-0.209882	-0.228489	0.180124
std	1.071217	0.974198	1.037486	1.063594	0.950325	0.990518	1.061623	1.007050	1.030406	0.945413	...	1.036097	0.911249	0.893855	0.871573	0.746652	0.777177	0.748119	0.777102	0.760788	0.384889
min	-0.885820	-0.885820	-0.885820	-0.786820	-0.786820	-0.885820	-0.885820	-0.885820	-0.754620	-0.885820	...	-0.885820	-0.885820	-0.786820	-0.885820	-0.885820	-0.885820	-0.885820	-0.885820	-0.885820	0.000000
25%	-0.490418	-0.493295	-0.485685	-0.497062	-0.496430	-0.491670	-0.493295	-0.494248	-0.490418	-0.514338	...	-0.513188	-0.494248	-0.513432	-0.500205	-0.515700	-0.514338	-0.519325	-0.514657	-0.525720	0.000000
50%	-0.391835	-0.406375	-0.385140	-0.393740	-0.395130	-0.401140	-0.393740	-0.407790	-0.394570	-0.422630	...	-0.412665	-0.401140	-0.415880	-0.407790	-0.431450	-0.422760	-0.435955	-0.431450	-0.437355	0.000000
75%	-0.252088	-0.262657	-0.245588	-0.249518	-0.252088	-0.262657	-0.251632	-0.266825	-0.251632	-0.283340	...	-0.266825	-0.265127	-0.281925	-0.280683	-0.304370	-0.304175	-0.308490	-0.309235	-0.312452	0.000000
max	5.199600	3.927100	4.740100	4.097600	4.889600	4.316300	6.270400	4.296300	5.157300	3.557100	...	4.954200	3.885200	3.231100	3.887900	3.556700	3.597800	3.745800	5.497700	3.744200	1.000000

	0	1	2	3	4	5	6	7	8	9	...	503	504	505	506	507	508	509	510	511	512
count	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	...	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000	139.000000
mean	0.080597	0.106864	-0.073066	0.090010	-0.017291	0.101628	-0.037029	-0.033965	-0.018947	-0.070171	...	-0.050990	-0.038306	0.021541	-0.078638	-0.078379	-0.126313	-0.052669	-0.061501	-0.241130	0.251799
std	1.141769	1.044256	0.875608	1.046712	1.027365	1.017212	0.924188	0.945663	0.990650	0.872831	...	0.857365	0.975202	1.016309	0.967131	0.997565	0.861918	1.066841	0.978974	0.699195	0.435616
min	-0.730190	-0.730190	-0.708330	-0.730190	-0.730190	-0.708330	-0.708330	-0.704200	-0.730190	-0.730190	...	-0.704200	-0.708330	-0.708330	-0.730190	-0.708330	-0.730190	-0.730190	-0.730190	-0.730190	0.000000
25%	-0.515040	-0.508000	-0.515065	-0.504435	-0.516775	-0.505260	-0.515040	-0.514535	-0.518805	-0.512920	...	-0.505260	-0.515670	-0.515670	-0.522115	-0.519205	-0.515065	-0.517755	-0.516650	-0.517755	0.000000
50%	-0.440290	-0.402760	-0.443280	-0.416700	-0.439580	-0.422380	-0.437600	-0.437600	-0.444280	-0.437600	...	-0.434140	-0.443280	-0.434140	-0.447690	-0.448390	-0.443280	-0.447200	-0.443280	-0.458360	0.000000
75%	-0.258280	-0.231685	-0.281875	-0.235360	-0.284545	-0.203335	-0.275410	-0.267860	-0.281875	-0.281875	...	-0.263180	-0.284545	-0.258280	-0.296125	-0.303080	-0.303080	-0.304165	-0.288355	-0.333030	0.500000
max	4.514400	3.061800	2.840900	4.257700	4.524400	3.816100	3.222700	4.402700	3.848100	2.579700	...	2.939300	4.460500	3.617300	3.387900	4.860500	3.997300	5.134200	4.379900	2.839600	1.000000

Task : Try to predict major earthquake event¶

Source¶

Richter Scale¶

Summary of the notebook:¶

1.Show me the data!¶

Testset: "Trainset, do you really know me well enough?"¶

Results:¶

Print a plot for each statistic in the data description¶

Final conclusions:¶

3. Show me the classes!¶

Print classes ratio¶

Plot few positive and negative examples from the train set¶

4. Features vs Features¶

Correlation matrix for both classes¶

Results:¶

Correlation matrix for Earthquake class (label 1)¶

Results:¶

Correlation matrix for No-Earthquake class (label 0)¶

Results:¶

Correlation matrices conclusions:¶

5. Features, describe yourselfs!¶

Same as in correlation matrices, first we will print plots for both of the classes together, then Earthquake class then No-Earthquake class¶

Both classes features boxplots¶

Results:¶

Label 0(No-Earthquake) features boxplots¶

Results:¶

Label 1 (Earthquake) features boxplot¶

Results¶

Final conclusions about features boxplot¶

6. Can we finally start playing with ML???¶

Split to features and labels¶

Try out of the box ML models¶

Results:¶

7. It's all about perspective¶

Transformation - Standardize features¶

Results:¶

Transformation - Fourier transformation¶

Results:¶

8. Now some deep learning (LSTM) for the fun¶

Results:¶

9. Run all combinations¶

Results:¶

10. If I had infinite amount of time - Future work¶

The end. It was fun!!¶